Evaluasi Kesesuaian Lahan Sawah Berdasarkan Status Hara di Kecamatan Seluma Selatan Kabupaten Seluma
Bibliographic record
Abstract
The low productivity of rice in Seluma Selatan District is caused by fertilization actions based on general recommendations and not based on site-specific recommendations. In fact, fertilization should be based on soil nutrient status, especially N, P and K. This study aims to determine the nutrient status and evaluate the suitability class of lowland rice in Seluma Selatan District, Seluma Regency. This study used survey methods, soil sampling, and laboratory analysis, then matched it with the soil fertility level classification system and land suitability classification. The results showed that the nutrient status of paddy fields in Seluma Selatan sub-district in low-medium rice fields was categorized. It is characterized by an acidic pH value and low P2O5 in providing nutrients for lowland rice plants. Evaluation of land suitability in Seluma Selatan sub-district shows that the land suitability classes are S2 and S3. The results of the evaluation of the most suitable land suitability for land unit 2 (Au.1.1.1) were quite suitable for S2n with available nutrient limiting factors at the P2O5 level and marginally suitable for S3n on land unit 4 (Hab.1.1.1) with available nutrient limiting factors at P2O5 and K2O levels. Efforts to improve the suitability class S2 (fairly suitable) can be increased to S1 class (very suitable) and the S3 land suitability class (marginally appropriate) can be increased to S2 (quite suitable) . Keywords: Land suitability, lowland rice, nutrient status
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".